code: def _feed_forward(c: TransformerConfigs):
      """
      Create feedforward layer configurations
      """
      conf = FeedForwardConfigs()
      conf.set_default(
          FeedForwardConfigs.d_model, func=lambda: c.d_model
      )
      conf.set_default(
          FeedForwardConfigs.dropout, func=lambda: c.dropout
      )
      return conf
  
imports: import copy
  import torch.nn
  from labml.configs import BaseConfigs, option, calculate, aggregate
  from labml_helpers.module import Module
  from .feed_forward import FeedForward
  from .mha import MultiHeadAttention
  from .models import EmbeddingsWithPositionalEncoding, EmbeddingsWithLearnedPositionalEncoding, TransformerLayer, Encoder, Decoder, Generator, EncoderDecoder
label_desc: Use image node_id 6 for calling a global function with example usage: _feed_forward(c) and returns: conf